A Paradigm Shift in Cloud-Seeding Research and Evaluation

Recent advances in cloud-seeding research have resulted in renewed interest in its viability and potential benefits for water management in the western U.S., as well as other regions of the world. These advances have transformed the ways in which we detect and evaluate the impacts of cloud seeding, resulting in a paradigm shift in the field of weather modification.

Traditionally, cloud seeding evaluation relied on statistical methods, which produce a relative impact estimate (e.g., a percentage change relative to the control, etc) at points with precipitation gauges. In addition, many of these studies have struggled to produce statistically significant results that overcome the high levels of natural variability in clouds and precipitation. Translating these point-based results onto spatial scales of watersheds over which the impacts are desired introduces even more uncertainty.

The Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment (SNOWIE) project collected unprecedented measurements that demonstrated seeding with silver iodide (AgI) produces ice crystals that grow and fall to the ground as snow. Unambiguous seeding signatures were initially detected by radar in three cases during SNOWIE. This observed signature enables a more detailed analysis of seeding impacts on precipitation in both magnitude and spatial extent using physical measurements that include in situ and remote sensing data as well as ground-based observations. Continued investigation of the SNOWIE dataset has revealed additional cases with distinct seeding signatures, which are currently under more detailed investigation. In addition, seeding signatures have now also been observed elsewhere in other airborne seeding programs. These comprehensive observations and analyses have confirmed the conceptual model of AgI seeding in winter orographic clouds to enhance precipitation and have been used to estimate the impacts of seeding over watershed scales.

In addition, computer modeling advances have improved our ability to simulate orographic precipitation and new model parameterizations have been developed to simulate the physical effects of AgI seeding. These simulations have been compared with observations from SNOWIE to verify that the model can realistically reproduce the cloud properties and precipitation enhancements as observed. The simulated results produce a spatially-distributed estimation of precipitation changes due to seeding, which overcomes the limitations of traditional point-based estimates from statistical methods.

This presentation will summarize these advances in both modeling and observations of seeding and demonstrate how they provide a framework for quantifying seeding impacts that is more useful for hydrological studies, which is often the ultimate goal for cloud seeding efforts.